

'''simple docstring'''





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 __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



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

						shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"							,			ignore_errors=__a	)  # 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	):
						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=__a	)

						# 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	):
						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=__a	)

						# 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"	)	)


def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)

	UpperCAmelCase__:  Any		       =   [
	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
	]
	UpperCAmelCase__:  List[Any]		       =   [
	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
	]

	for i in range(1  ,len_source_seq + 1			):
		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"

	for i in range(1  ,len_destination_seq + 1			):
		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"

	for i in range(1  ,len_source_seq + 1			):
		for j in range(1  ,len_destination_seq + 1			):
			if source_seq[i - 1] == destination_seq[j - 1]:
				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
			else:
				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)

			if costs[i - 1][j] + delete_cost < costs[i][j]:
				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"

			if costs[i][j - 1] + insert_cost < costs[i][j]:
				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"

	return costs, ops



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if i == 0 and j == 0:
		return []
	else:
		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
			seq.append(ops[i][j]			)
			return seq
		elif ops[i][j][0] == "D":
			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
			seq.append(ops[i][j]			)
			return seq
		else:
			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
			seq.append(ops[i][j]			)
			return seq


if __name__ == "__main__":
					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

					_lowerCAmelCase     :			List[Any]    =len(operations)
					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)

					_lowerCAmelCase     :			int    =list("""Python""")
					_lowerCAmelCase     :			Optional[int]    =0
					_lowerCAmelCase     :			List[Any]    =0

					with open("""min_cost.txt""", """w""") as file:
										for op in sequence:
															print("""""".join(string))

															if op[0] == "C":
																				file.write("""%-16s""" % """Copy %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost -= 1
															elif op[0] == "R":
																				_lowerCAmelCase     :			Tuple    =op[2]

																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
																				file.write("""\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 1
															elif op[0] == "D":
																				string.pop(i)

																				file.write("""%-16s""" % """Delete %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 2
															else:
																				string.insert(i, op[1])

																				file.write("""%-16s""" % """Insert %c""" % op[1])
																				file.write("""\t\t\t""" + """""".join(string))
																				file.write("""\r\n""")

																				cost += 2

															i += 1

										print("""""".join(string))
										print("""Cost: """, cost)

										file.write("""\r\nMinimum cost: """ + str(cost))