"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None    ):






										"""simple docstring"""

										snake_case								=			list(poly_a or [0]    )[:]
										snake_case								=			list(poly_b or [0]    )[:]

										# Remove leading zero coefficients
										while self.polyA[-1] == 0:
																self.polyA.pop()
										snake_case								=			len(self.polyA    )

										while self.polyB[-1] == 0:
																self.polyB.pop()
										snake_case								=			len(self.polyB    )

										# Add 0 to make lengths equal a power of 2
										snake_case								=			int(
										    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

										while len(self.polyA    ) < self.c_max_length:
																self.polyA.append(0    )
										while len(self.polyB    ) < self.c_max_length:
																self.polyB.append(0    )
										# A complex root used for the fourier transform
										snake_case								=			complex(mpmath.root(x=1		,    n=self.c_max_length		,    k=1    )    )

										# The product
										snake_case								=			self.__multiply()

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
										# Corner case
										if len(lowerCAmelCase    ) <= 1:
																return dft[0]
										#
										snake_case								=			self.c_max_length // 2
										while next_ncol > 0:
																snake_case								=			[[] for i in range(lowerCAmelCase    )]
																snake_case								=			self.root**next_ncol

																# First half of next step
																snake_case								=			1
																for j in range(self.c_max_length // (next_ncol * 2)    ):
																						for i in range(lowerCAmelCase    ):
																												new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																						current_root *= root
																# Second half of next step
																snake_case								=			1
																for j in range(self.c_max_length // (next_ncol * 2)    ):
																						for i in range(lowerCAmelCase    ):
																												new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																						current_root *= root
																# Update
																snake_case								=			new_dft
																snake_case								=			next_ncol // 2
										return dft[0]

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.__dft('A'    )
										snake_case								=			self.__dft('B'    )
										snake_case								=			[[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
										del dft_a
										del dft_b

										# Corner Case
										if len(inverce_c[0]    ) <= 1:
																return inverce_c[0]
										# Inverse DFT
										snake_case								=			2
										while next_ncol <= self.c_max_length:
																snake_case								=			[[] for i in range(lowerCAmelCase    )]
																snake_case								=			self.root ** (next_ncol // 2)
																snake_case								=			1
																# First half of next step
																for j in range(self.c_max_length // next_ncol    ):
																						for i in range(next_ncol // 2    ):
																												# Even positions
																												new_inverse_c[i].append(
																												    (
																												        inverce_c[i][j]
																												        + inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / 2    )
																												# Odd positions
																												new_inverse_c[i + next_ncol // 2].append(
																												    (
																												        inverce_c[i][j]
																												        - inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / (2 * current_root)    )
																						current_root *= root
																# Update
																snake_case								=			new_inverse_c
																next_ncol *= 2
										# Unpack
										snake_case								=			[round(x[0].real		,    8    ) + round(x[0].imag		,    8    ) * 1j for x in inverce_c]

										# Remove leading 0's
										while inverce_c[-1] == 0:
																inverce_c.pop()
										return inverce_c

				def __str__(				self    ):






										"""simple docstring"""

										snake_case								=			'A = ' + ' + '.join(
										    F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]    )    )
										snake_case								=			'B = ' + ' + '.join(
										    F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]    )    )
										snake_case								=			'A*B = ' + ' + '.join(
										    F"""{coef}*x^{i}""" for coef, i in enumerate(self.product    )    )

										return F"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()



"""simple docstring"""
import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

										# Force fetching the files so that we can use the cache
										snake_case								=			'hf-internal-testing/tiny-random-bert'
										BertConfig.from_pretrained(lowerCAmelCase    )
										BertModel.from_pretrained(lowerCAmelCase    )
										BertTokenizer.from_pretrained(lowerCAmelCase    )
										pipeline(task='fill-mask'		,    model=lowerCAmelCase    )

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run, mock]    )]

										# should succeed
										snake_case								=			self.get_env()
										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

										# Force fetching the files so that we can use the cache
										snake_case								=			'hf-internal-testing/tiny-random-bert'
										BertConfig.from_pretrained(lowerCAmelCase    )
										BertModel.from_pretrained(lowerCAmelCase    )
										BertTokenizer.from_pretrained(lowerCAmelCase    )
										pipeline(task='fill-mask'		,    model=lowerCAmelCase    )

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run, mock]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

										# next emulate no network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, mock, run]    )]

										# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
										# env["TRANSFORMERS_OFFLINE"] = "0"
										# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
										# self.assertEqual(result.returncode, 1, result.stderr)

										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import pipeline\n        '
										snake_case								=			'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

										snake_case								=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
										snake_case								=			self.get_env()
										snake_case								=			'1'
										snake_case								=			[sys.executable, '-c', '\n'.join([load, mock, run]    )]
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    1		,    result.stderr    )
										self.assertIn(
										    'You cannot infer task automatically within `pipeline` when using offline mode'		,    result.stderr.decode().replace('\n'		,    ''    )		,    )

				@require_torch
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			'\nfrom transformers import AutoModel\n        '
										snake_case								=			'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

										# baseline - just load from_pretrained with normal network
										snake_case								=			[sys.executable, '-c', '\n'.join([load, run]    )]

										# should succeed
										snake_case								=			self.get_env()
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )

										# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
										snake_case								=			'1'
										snake_case								=			subprocess.run(lowerCAmelCase		,    env=lowerCAmelCase		,    check=lowerCAmelCase		,    capture_output=lowerCAmelCase    )
										self.assertEqual(result.returncode		,    0		,    result.stderr    )
										self.assertIn('success'		,    result.stdout.decode()    )


