

import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 	__magic_name__				(       unittest.TestCase							):






     @patch("sys.argv"		,	["fakeprogrampath", "env"]  )
     def   UpperCAmelCase_							(							self  )->    int:
          import transformers.commands.transformers_cli

          with CaptureStd() as cs:
               transformers.commands.transformers_cli.main()
          self.assertIn("Python version"		,	cs.out  )
          self.assertIn("Platform"		,	cs.out  )
          self.assertIn("Using distributed or parallel set-up in script?"		,	cs.out  )






     @is_pt_tf_cross_test
     @patch(
         "sys.argv"		,	["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]  )
     def   UpperCAmelCase_							(							self  )->    List[Any]:
          import transformers.commands.transformers_cli

          shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"		,	ignore_errors=lowerCamelCase__  )  # cleans potential past runs
          transformers.commands.transformers_cli.main()

          # The original repo has no TF weights -- if they exist, they were created by the CLI
          self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"  )  )






     @require_torch
     @patch("sys.argv"		,	["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]  )
     def   UpperCAmelCase_							(							self  )->    Dict:
          import transformers.commands.transformers_cli

          # # remove any previously downloaded model to start clean
          shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"		,	ignore_errors=lowerCamelCase__  )

          # run the command
          transformers.commands.transformers_cli.main()

          # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"  )  )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"  )  )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"  )  )






     @require_torch
     @patch(
         "sys.argv"		,	[
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]		,	)
     def   UpperCAmelCase_							(							self  )->    str:
          import transformers.commands.transformers_cli

          # # remove any previously downloaded model to start clean
          shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"		,	ignore_errors=lowerCamelCase__  )

          # run the command
          transformers.commands.transformers_cli.main()

          # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"  )  )
          self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"  )  )
          self.assertTrue(
              os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"  )  )



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE		:Any				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :str								=			"""blenderbot"""
     UpperCamelCase_  :Union[str, Any]								=			["""past_key_values"""]
     UpperCamelCase_  :Any								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(							self		,	_lowercase=8_008		,	_lowercase=128		,	_lowercase=2		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=24		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=2_560		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=1		,	_lowercase=False		,	_lowercase=0		,	_lowercase=1		,	_lowercase=2		,	_lowercase=3		,	_lowercase=2		,	**_lowercase		,	)->    List[str]:
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	d_model
         UpperCamelCase_      =	encoder_ffn_dim
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	encoder_attention_heads
         UpperCamelCase_      =	decoder_ffn_dim
         UpperCamelCase_      =	decoder_layers
         UpperCamelCase_      =	decoder_attention_heads
         UpperCamelCase_      =	dropout
         UpperCamelCase_      =	attention_dropout
         UpperCamelCase_      =	activation_dropout
         UpperCamelCase_      =	activation_function
         UpperCamelCase_      =	init_std
         UpperCamelCase_      =	encoder_layerdrop
         UpperCamelCase_      =	decoder_layerdrop
         UpperCamelCase_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=_lowercase		,	bos_token_id=_lowercase		,	eos_token_id=_lowercase		,	is_encoder_decoder=_lowercase		,	decoder_start_token_id=_lowercase		,	encoder_no_repeat_ngram_size=_lowercase		,	forced_eos_token_id=_lowercase		,	**_lowercase		,	)






class 	__magic_name__				(       snake_case							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_      =	{0: "batch"}
                 UpperCamelCase_      =	{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
             if self.use_past:
                 self.fill_with_past_key_values_(_lowercase		,	direction="inputs"  )
         elif self.task == "causal-lm":
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         else:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                     ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                 ]  )

         return common_inputs






     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	dict(**_lowercase		,	**_lowercase  )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
             else:
                 import torch
             UpperCamelCase_     ,					UpperCamelCase_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	common_inputs["decoder_input_ids"].shape[1]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	decoder_seq_length
             UpperCamelCase_      =	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_lowercase		,	_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

             for _ in range(_lowercase  ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                     )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )

         if self.use_past:
             if not is_torch_available():
                 raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
             else:
                 import torch
             UpperCamelCase_     ,					UpperCamelCase_      =	common_inputs["input_ids"].shape
             UpperCamelCase_      =	seqlen
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	common_inputs["attention_mask"].dtype
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_batch		,	num_token_to_add=0  )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCamelCase_      =	tokenizer.num_special_tokens_to_add(_lowercase  )
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=_lowercase  )

         # Generate dummy inputs according to compute batch and sequence
         UpperCamelCase_      =	[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
         UpperCamelCase_      =	dict(tokenizer(_lowercase		,	return_tensors=_lowercase  )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )

         elif self.task == "causal-lm":
             UpperCamelCase_      =	self._generate_dummy_inputs_for_causal_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )
         else:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )

         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  )._flatten_past_key_values_(
                 _lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Union[str, Any]:
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"  )

         UpperCamelCase_      =	"past_key_values" if direction == "inputs" else "present"
         UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

         UpperCamelCase_      =	"past_encoder_sequence"
         UpperCamelCase_      =	"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

         for i in range(_lowercase  ):
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
