

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 __A     :   List[Any]    	=			'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None				, _UpperCamelCase=None  )							->			Optional[int]:



  """simple docstring"""






  if attention_mask is None:
    _snake_case		=							np.where(input_ids != config.pad_token_id				, 1				, 0  )
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
  }



@require_flax
class 						lowercase_					:



      def __init__(    self   :       Tuple    ,      A__   :       Union[str, Any]    ,      A__   :       str=13    ,      A__   :       str=7    ,      A__   :       Union[str, Any]=True    ,      A__   :       Dict=False    ,      A__   :       Union[str, Any]=99    ,      A__   :       str=16    ,      A__   :       Any=2    ,      A__   :       Union[str, Any]=4    ,      A__   :       Dict=4    ,      A__   :       List[str]="gelu"    ,      A__   :       Any=0.1    ,      A__   :       Optional[int]=0.1    ,      A__   :       Any=20    ,      A__   :       List[Any]=2    ,      A__   :       int=1    ,      A__   :       Any=0    ,      A__   :       Tuple=16    ,      A__   :       Tuple=16    ,      A__   :       int=0.02    ,      )					->  str:
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							seq_length
        _snake_case		=							is_training
        _snake_case		=							use_labels
        _snake_case		=							vocab_size
        _snake_case		=							hidden_size
        _snake_case		=							num_hidden_layers
        _snake_case		=							num_attention_heads
        _snake_case		=							intermediate_size
        _snake_case		=							hidden_act
        _snake_case		=							hidden_dropout_prob
        _snake_case		=							attention_probs_dropout_prob
        _snake_case		=							max_position_embeddings
        _snake_case		=							eos_token_id
        _snake_case		=							pad_token_id
        _snake_case		=							bos_token_id
        _snake_case		=							embed_dim
        _snake_case		=							word_embed_proj_dim
        _snake_case		=							initializer_range
        _snake_case		=							False



      def 		UpperCamelCase_	(    self   :       str	)					->  int:
        _snake_case		=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size	)    ,      3    ,      self.vocab_size	)
        _snake_case		=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,      dtype=np.intaa	))    ,      -1	)

        _snake_case		=							OPTConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      ffn_dim=self.intermediate_size    ,      dropout=self.hidden_dropout_prob    ,      attention_dropout=self.attention_probs_dropout_prob    ,      max_position_embeddings=self.max_position_embeddings    ,      eos_token_id=self.eos_token_id    ,      bos_token_id=self.bos_token_id    ,      pad_token_id=self.pad_token_id    ,      embed_dim=self.embed_dim    ,      is_encoder_decoder=lowercase_    ,      word_embed_proj_dim=self.word_embed_proj_dim    ,      initializer_range=self.initializer_range    ,      use_cache=lowercase_    ,      )
        _snake_case		=							prepare_opt_inputs_dict(lowercase_    ,      lowercase_	)
        return config, inputs_dict



      def 		UpperCamelCase_	(    self   :       Dict	)					->  Tuple:
        _snake_case		=							self.prepare_config_and_inputs()
        return config, inputs_dict



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Any    ,      A__   :       Dict    ,      A__   :       List[str]	)					->  Optional[Any]:
        _snake_case		=							20
        _snake_case		=							model_class_name(lowercase_	)

        _snake_case		=							inputs_dict['''input_ids''']
        _snake_case		=							inputs_dict['''attention_mask''']

        _snake_case		=							model.init_cache(input_ids.shape[0]    ,      lowercase_	)
        _snake_case		=							jnp.ones((input_ids.shape[0], max_length)    ,      dtype='''i4'''	)

        _snake_case		=							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)    ,      )
        _snake_case		=							model(
            input_ids[:, :-1]    ,      attention_mask=lowercase_    ,      past_key_values=lowercase_    ,      position_ids=lowercase_    ,      )

        _snake_case		=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''	)
        _snake_case		=							model(
            input_ids[:, -1:]    ,      attention_mask=lowercase_    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=lowercase_    ,      )

        _snake_case		=							model(lowercase_	)

        _snake_case		=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3    ,      msg=f"""Max diff is {diff}"""	)







      def 		UpperCamelCase_	(    self   :       int    ,      A__   :       Optional[int]    ,      A__   :       Any    ,      A__   :       str	)					->  Tuple:
        _snake_case		=							20
        _snake_case		=							model_class_name(lowercase_	)

        _snake_case		=							(
            inputs_dict['''input_ids'''],
            inputs_dict['''attention_mask'''],
        )

        _snake_case		=							jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
            ]    ,      axis=-1    ,      )

        _snake_case		=							model.init_cache(input_ids.shape[0]    ,      lowercase_	)
        _snake_case		=							jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)    ,      )

        _snake_case		=							model(
            input_ids[:, :-1]    ,      attention_mask=lowercase_    ,      past_key_values=lowercase_    ,      position_ids=lowercase_    ,      )
        _snake_case		=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype='''i4'''	)
        _snake_case		=							model(
            input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=lowercase_    ,      position_ids=lowercase_    ,      )

        _snake_case		=							model(lowercase_    ,      attention_mask=lowercase_	)

        _snake_case		=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
        self.parent.assertTrue(diff < 1e-3    ,      msg=f"""Max diff is {diff}"""	)



@require_flax
class 						lowercase_					(						__UpperCAmelCase     ,							unittest.TestCase     ,							__UpperCAmelCase  ):
      UpperCamelCase_    :	Tuple		   =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      UpperCamelCase_    :	str		   =       () if is_flax_available() else ()



      def 		UpperCamelCase_	(    self   :       int	)					->  Optional[Any]:
        _snake_case		=							FlaxOPTModelTester(self	)



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Dict:
        _snake_case		=							self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(lowercase_    ,      lowercase_    ,      lowercase_	)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Union[str, Any]:
        _snake_case		=							self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_    ,      lowercase_    ,      lowercase_	)







      @slow
      def 		UpperCamelCase_	(    self   :       List[str]	)					->  Optional[Any]:

        for model_class_name in self.all_model_classes:
          _snake_case		=							model_class_name.from_pretrained('''facebook/opt-125m'''	)
          _snake_case		=							np.ones((1, 1)	) * model.config.eos_token_id
          _snake_case		=							model(lowercase_	)
          self.assertIsNotNone(lowercase_	)



@require_sentencepiece
@require_flax
class 						lowercase_					(						unittest.TestCase  ):



      @slow
      def 		UpperCamelCase_	(    self   :       List[str]	)					->  List[str]:
        _snake_case		=							FlaxOPTModel.from_pretrained('''facebook/opt-350m'''	)
        _snake_case		=							jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	)
        _snake_case		=							model(input_ids=lowercase_	).last_hidden_state
        _snake_case		=							(1, 11, 512)
        self.assertEqual(output.shape    ,      lowercase_	)
        _snake_case		=							jnp.array(
            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]	)
        self.assertTrue(jnp.allclose(output[:, :3, :3]    ,      lowercase_    ,      atol=4e-2	)	)



@require_flax
@slow
class 						lowercase_					(						unittest.TestCase  ):



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        super().setUp()
        _snake_case		=							'''facebook/opt-350m'''







      def 		UpperCamelCase_	(    self   :       str	)					->  Union[str, Any]:
        _snake_case		=							FlaxOPTForCausalLM.from_pretrained(self.path_model	)
        _snake_case		=							GPTaTokenizer.from_pretrained(self.path_model	)

        _snake_case		=							[
            '''Today is a beautiful day and I want to''',
            '''In the city of''',
            '''Paris is the capital of France and''',
            '''Computers and mobile phones have taken''',
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        _snake_case		=							tokenizer(lowercase_    ,      return_tensors='''jax'''    ,      padding=lowercase_    ,      add_special_tokens=lowercase_	)
        _snake_case		=							model(inputs.input_ids    ,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        _snake_case		=							jnp.array(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]	)
        self.assertTrue(jnp.allclose(lowercase_    ,      lowercase_    ,      atol=4e-2	)	)

        _snake_case		=							jax.jit(lowercase_	)
        _snake_case		=							model(inputs.input_ids    ,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        self.assertTrue(jnp.allclose(lowercase_    ,      lowercase_    ,      atol=4e-2	)	)



@require_flax
@slow
class 						lowercase_					(						unittest.TestCase  ):



      @property
      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Optional[Any]:
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        _snake_case		=							'''facebook/opt-125m'''

        _snake_case		=							[
            '''Today is a beautiful day and I want to''',
            '''In the city of New York, the city''',
            '''Paris is the capital of France and the capital''',
            '''Computers and mobile phones have taken over the''',
        ]

        _snake_case		=							[]

        _snake_case		=							FlaxOPTForCausalLM.from_pretrained(lowercase_	)
        _snake_case		=							GPTaTokenizer.from_pretrained(lowercase_	)

        for prompt in self.prompts:
          _snake_case		=							tokenizer(lowercase_    ,      return_tensors='''jax'''	).input_ids

          _snake_case		=							model.generate(lowercase_    ,      max_length=10	)
          _snake_case		=							generated_ids[0]

          _snake_case		=							tokenizer.batch_decode(lowercase_    ,      skip_special_tokens=lowercase_	)
          predicted_outputs += generated_string

        self.assertListEqual(lowercase_    ,      lowercase_	)



      def 		UpperCamelCase_	(    self   :       Dict	)					->  int:
        _snake_case		=							'''facebook/opt-350m'''

        _snake_case		=							[
            '''Today is a beautiful day and I want to''',
            '''In the city of San Francisco, the city''',
            '''Paris is the capital of France and the capital''',
            '''Computers and mobile phones have taken over the''',
        ]

        _snake_case		=							[]
        _snake_case		=							FlaxOPTForCausalLM.from_pretrained(lowercase_	)
        _snake_case		=							GPTaTokenizer.from_pretrained(lowercase_	)

        for prompt in self.prompts:
          _snake_case		=							tokenizer(lowercase_    ,      return_tensors='''jax'''	).input_ids

          _snake_case		=							model.generate(lowercase_    ,      max_length=10	)
          _snake_case		=							generated_ids[0]

          _snake_case		=							tokenizer.batch_decode(lowercase_    ,      skip_special_tokens=lowercase_	)
          predicted_outputs += generated_string

        self.assertListEqual(lowercase_    ,      lowercase_	)



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  int:
        _snake_case		=							'''facebook/opt-125m'''
        _snake_case		=							[
            '''Today is a beautiful day and I want to thank''',
            '''In the city of Rome Canaver Canaver Canaver Canaver''',
        ]
        _snake_case		=							FlaxOPTForCausalLM.from_pretrained(lowercase_	)
        _snake_case		=							GPTaTokenizer.from_pretrained(lowercase_	)
        _snake_case		=							tokenizer(
            [
                '''Today is a beautiful day and I want to''',
                '''In the city of''',
            ]    ,      return_tensors='''jax'''    ,      padding=lowercase_    ,      )

        _snake_case		=							jax.jit(model.generate	)

        _snake_case		=							jit_generate(inputs['''input_ids''']    ,      attention_mask=inputs['''attention_mask''']	).sequences

        _snake_case		=							tokenizer.batch_decode(lowercase_    ,      skip_special_tokens=lowercase_	)

        self.assertIsNotNone(lowercase_    ,      lowercase_	)







      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Tuple:
        _snake_case		=							'''facebook/opt-350m'''

        _snake_case		=							GPTaTokenizer.from_pretrained(lowercase_	)
        _snake_case		=							FlaxOPTForCausalLM.from_pretrained(lowercase_	)

        _snake_case		=							'''left'''

        # use different length sentences to test batching
        _snake_case		=							[
            '''Hello, my dog is a little''',
            '''Today, I''',
        ]

        _snake_case		=							tokenizer(lowercase_    ,      return_tensors='''jax'''    ,      padding=lowercase_	)
        _snake_case		=							inputs['''input_ids''']

        _snake_case		=							model.generate(input_ids=lowercase_    ,      attention_mask=inputs['''attention_mask''']    ,      trace=lowercase_	)

        _snake_case		=							tokenizer(sentences[0]    ,      return_tensors='''jax'''	).input_ids
        _snake_case		=							model.generate(input_ids=lowercase_	)

        _snake_case		=							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
        _snake_case		=							tokenizer(sentences[1]    ,      return_tensors='''jax'''	).input_ids
        _snake_case		=							model.generate(input_ids=lowercase_    ,      max_length=model.config.max_length - num_paddings	)

        _snake_case		=							tokenizer.batch_decode(outputs[0]    ,      skip_special_tokens=lowercase_	)
        _snake_case		=							tokenizer.decode(output_non_padded[0][0]    ,      skip_special_tokens=lowercase_	)
        _snake_case		=							tokenizer.decode(output_padded[0][0]    ,      skip_special_tokens=lowercase_	)

        _snake_case		=							[
            '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
            '''Today, I was in the middle of a conversation with a friend about the''',
        ]
        self.assertListEqual(lowercase_    ,      lowercase_	)
        self.assertListEqual(lowercase_    ,      [non_padded_sentence, padded_sentence]	)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_weight.chunk(3				, dim=0  )
  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_bias.chunk(3				, dim=0  )

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






  copy_linear(hf_mlp.fca				, pt_mlp.c_fc  )
  copy_linear(hf_mlp.fca				, pt_mlp.c_proj  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )
  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )

  # copy MLP
  copy_mlp(hf_layer.mlp				, pt_layer.mlp  )

  # copy attn
  copy_attn_layer(hf_layer.self_attn				, pt_layer.attn  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  for hf_layer, pt_layer in zip(_UpperCamelCase				, _UpperCamelCase  ):
    copy_layer(_UpperCamelCase				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _snake_case		=							pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm				, pt_model.ln_final  )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers				, pt_model.transformer.resblocks  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm				, pt_model.visual.ln_pre  )
  copy_linear(hf_model.vision_model.post_layernorm				, pt_model.visual.ln_post  )

  # copy embeds
  _snake_case		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _snake_case		=							pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers				, pt_model.visual.transformer.resblocks  )



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






  if config_path is not None:
    _snake_case		=							CLIPConfig.from_pretrained(_UpperCamelCase  )
  else:
    _snake_case		=							CLIPConfig(projection_dim=512				, text_config={}				, vision_config={}  )

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

  _snake_case		=							torch.arange(0				, 77  ).unsqueeze(0  )
  _snake_case		=							torch.randn(1				, 3				, 224				, 224  )

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 __A    	=			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

